Chadwick P. Ward

The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods

The Alzheimers Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimers disease. Magnetic resonance imaging (MRI), (18F)‐fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquiredat multiple time points. All data will be cross‐linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications thatguided protocol development. A major effort was devoted toevaluating 3D T1‐weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced‐scale clinical trial. The protocol selected for the ADNI study includes: back‐to‐back 3D magnetization prepared rapid gradient echo (MP‐RAGE) scans; B1‐calibration scans when applicable; and an axial proton density‐T2 dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom‐based monitoring of all scanners could be used as a model for other multisite trials. J. Magn. Reson. Imaging 2008.

Longitudinal stability of MRI for mapping brain change using tensor-based morphometry

Measures of brain change can be computed from sequential MRI scans, providing valuable information on disease progression, e.g., for patient monitoring and drug trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy, but its sensitivity depends on the contrast and geometric stability of the images. As part of the Alzheimers Disease Neuroimaging Initiative (ADNI), 17 normal elderly subjects were scanned twice (at a 2-week interval) with several 3D 1.5 T MRI pulse sequences: high and low flip angle SPGR/FLASH (from which Synthetic T1 images were generated), MP-RAGE, IR-SPGR (N = 10) and MEDIC (N = 7) scans. For each subject and scan type, a 3D deformation map aligned baseline and follow-up scans, computed with a nonlinear, inverse-consistent elastic registration algorithm. Voxelwise statistics, in ICBM stereotaxic space, visualized the profile of mean absolute change and its cross-subject variance; these maps were then compared using permutation testing. Image stability depended on: (1) the pulse sequence; (2) the transmit/receive coil type (birdcage versus phased array); (3) spatial distortion corrections (using MEDIC sequence information); (4) B1-field intensity inhomogeneity correction (using N3). SPGR/FLASH images acquired using a birdcage coil had least overall deviation. N3 correction reduced coil type and pulse sequence differences and improved scan reproducibility, except for Synthetic T1 images (which were intrinsically corrected for B1-inhomogeneity). No strong evidence favored B0 correction. Although SPGR/FLASH images showed least deviation here, pulse sequence selection for the ADNI project was based on multiple additional image analyses, to be reported elsewhere.

3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimers disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment

Objective: To compare volumetric MRI of whole brain and medial temporal lobe structures to clinical measures for predicting progression from amnestic mild cognitive impairment (MCI) to Alzheimer disease (AD). Methods: Baseline MRI scans from 129 subjects with amnestic MCI were obtained from participants in the Alzheimers Disease Cooperative Study groups randomized, placebo-controlled clinical drug trial of donepezil, vitamin E, or placebo. Measures of whole brain, ventricular, hippocampal, and entorhinal cortex volumes were acquired. Participants were followed with clinical and cognitive evaluations until formal criteria for AD were met, or completion of 36 months of follow-up. Logistic regression modeling was done to assess the predictive value of all MRI measures, risk factors such as APOE genotype, age, family history of AD, education, sex, and cognitive test scores for progression to AD. Least angle regression modeling was used to determine which variables would produce an optimal predictive model, and whether adding MRI measures to a model with only clinical measures would improve predictive accuracy. Results: Of the four MRI measures evaluated, only ventricular volumes and hippocampal volumes were predictive of progression to AD. Maximal predictive accuracy using only MRI measures was obtained by hippocampal volumes by themselves (60.4%). When clinical variables were added to the model, the predictive accuracy increased to 78.8%. Use of MRI measures did not improve predictive accuracy beyond that obtained by cognitive measures alone. APOE status, MRI, or demographic variables were not necessary for the optimal predictive model. This optimal model included the Delayed 10-word list recall, New York University Delayed Paragraph Recall, and the Alzheimers Disease Assessment Scale–Cognitive Subscale total score. Conclusion: In moderate stages of amnestic mild cognitive impairment, common cognitive tests provide better predictive accuracy than measures of whole brain, ventricular, entorhinal cortex, or hippocampal volumes for assessing progression to Alzheimer disease.

Alzheimer’s Disease Neuroimaging Initiative: A one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition

Tensor-based morphometry can recover three-dimensional longitudinal brain changes over time by nonlinearly registering baseline to follow-up MRI scans of the same subject. Here, we compared the anatomical distribution of longitudinal brain structural changes, over 12 months, using a subset of the ADNI dataset consisting of 20 patients with Alzheimers disease (AD), 40 healthy elderly controls, and 40 individuals with mild cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map) was created using an unbiased registration technique, and spatially normalized to a geometrically-centered average image based on healthy controls. Voxelwise statistical analyses revealed regional differences in atrophy rates, and these differences were correlated with clinical measures and biomarkers. Consistent with prior studies, we detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental state exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning memory scores. In AD, temporal atrophy rates were correlated with several biomarker indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid 1-42 (ABeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.

Longitudinal MRI findings from the vitamin E and Donepezil treatment study for MCI

The vitamin E and donepezil trial for the treatment of amnestic mild cognitive impairment (MCI) was conducted at 69 centers in North America; 24 centers participated in an MRI sub study. The objective of this study was to evaluate the effect of treatment on MRI atrophy rates; and validate rate measures from serial MRI as indicators of disease progression in multi center therapeutic trials for MCI. Annual percent change (APC) from baseline to follow-up was measured for hippocampus, entorhinal cortex, whole brain, and ventricle in the 131 subjects who remained in the treatment study and completed technically satisfactory baseline and follow-up scans. Although a non-significant trend toward slowing of hippocampal atrophy rates was seen in APOE is an element of 4 carriers treated with donepezil; no treatment effect was confirmed for any MRI measure in either treatment group. For each of the four brain atrophy rate measures, APCs were greater in subjects who converted to AD than non-converters, and were greater in APOE is an element of 4 carriers than non-carriers. MRI APCs and changes in cognitive test performance were uniformly correlated in the expected direction (all p<0.000). Results of this study support the feasibility of using MRI as an outcome measure of disease progression in multi center therapeutic trials for MCI.

Measurement of MRI scanner performance with the ADNI phantom.

The objectives of this study are as follows: to describe practical implementation challenges of multisite, multivendor quantitative studies; to describe the MRI phantom and analysis software used in the Alzheimers Disease Neuroimaging Initiative (ADNI) study, illustrate the utility of the system for measuring scanner performance, the ability to assess gradient field nonlinearity corrections: and to recover human brain images without geometric scaling errors in multisite studies. ADNI is a large multicenter study with each center having its own copy of the phantom. The design of the phantom and analysis software are presented as results from predistribution systematics studies and results from field experience with the phantom at 58 enrolling ADNI sites over a 3 year period. The estimated coefficients of variation intrinsic to measurements of geometry in a single phantom are in the range of 3-5 parts in 10(4). Phantom measurements accurately detect linear and nonlinear scaling in images. Gradient unwarping methods are readily assessed by phantom nonlinearity measurements. Phantom-based scaling correction reduces observed geometric drift in human images by one-third or more. Repair or replacement of phantoms between scans, however, is a confounding factor. The ADNI phantom can be used to assess both scanner performance and the validity of postprocessing image corrections in order to reduce systematic errors in human images. Reduced measurement errors should decrease measurement bias and increase statistical power for measurements of rates of change in the brain structure in AD treatment trials. Perhaps the greatest practical value of incorporating ADNI phantom measurements in a multisite study is to identify scanner errors through central monitoring. This approach has resulted in identification of system errors including sites misidentification of their own gradient hardware and the disabling of autoshim, and a miscalibrated laser alignment light. If undetected, these errors would have contributed to imprecision in quantitative metrics at over 25% of all enrolling ADNI sites.

Automatic quality assessment in structural brain magnetic resonance imaging

MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer‐aided diagnosis. This work proposes a fully‐automatic method for measuring image quality of three‐dimensional (3D) structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, and ghosting. The method has been validated on 749 3D T1‐weighted 1.5T and 3T head scans acquired at 36 Alzheimers Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85%). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule out the need for a repeat scan while the patient is still in the magnet bore. Magn Reson Med, 2009.

Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI

Measuring rates of brain atrophy from serial magnetic resonance imaging (MRI) studies is an attractive way to assess disease progression in neurodegenerative disorders, particularly Alzheimers disease (AD). A widely recognized approach is the boundary shift integral (BSI). The objective of this study was to evaluate how several common scan non-idealities affect the output of the BSI algorithm. We created three types of image non-idealities between the image volumes in a serial pair used to measure between-scan change: inconsistent image contrast between serial scans, head motion, and poor signal-to-noise (SNR). In theory the BSI volume difference measured between each pair of images should be zero and any deviation from zero should represent corruption of the BSI measurement by some non-ideality intentionally introduced into the second scan in the pair. Two different BSI measures were evaluated, whole brain and ventricle. As the severity of motion, noise, and non-congruent image contrast increased in the second scan, the calculated BSI values deviated progressively more from the expected value of zero. This study illustrates the magnitude of the error in measures of change in brain and ventricle volume across serial MRI scans that can result from commonly encountered deviations from ideal image quality. The magnitudes of some of the measurement errors seen in this study exceed the disease effect in AD shown in various publications, which range from 1% to 2.78% per year for whole brain atrophy and 5.4% to 13.8% per year for ventricle expansion (Table 1). For example, measurement error may exceed 100% if image contrast properties dramatically differ between the two scans in a measurement pair. Methods to maximize consistency of image quality over time are an essential component of any quantitative serial MRI study.

A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity

Alzheimers disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, “AD signature” measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12.